System

Abstract

An object of a system according to an example embodiment is to analyze flow line data and information on product display in a store and propose efficient and attractive display or arrangement.SOLUTION: A system includes a collection unit, an analysis unit, and a proposal unit. The collection part collects flow line data in a store and information on commodity display. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes efficient and attractive display or arrangement on the basis of the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has not yet been able to effectively utilize in-store traffic flow data and product display information to optimize store design, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze in-store traffic flow data and product display information, and propose efficient and attractive displays or layouts. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects in-store traffic flow data and product display information. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an efficient and attractive display or layout based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze in-store traffic flow data and product display information, and propose efficient and attractive displays or layouts. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A store design proposal system according to an embodiment of the present invention proposes an efficient and attractive store design based on in-store traffic flow data and product display information. The store design proposal system collects in-store traffic flow data and product display information, analyzes them using AI, and proposes efficient and attractive displays and layouts. For example, the store design proposal system collects customer traffic flow data and product display information using in-store cameras and sensors. Next, the store design proposal system analyzes the collected data using AI to understand customer behavior patterns. Furthermore, based on the analysis results, the store design proposal system proposes placing popular products in aisles frequently visited by customers. This allows the store design proposal system to improve customer experience and sales efficiency. This allows the store design proposal system to realize an optimal store design backed by data and strengthen competitiveness. For example, by proposing efficient displays and layouts based on customer traffic flow data, customer experience and sales efficiency can be improved. Furthermore, by carefully arranging products, customer purchasing motivation can be increased. This allows the store design proposal system to strengthen competitiveness and increase sales.

[0029] A store design proposal system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects in-store traffic flow data and product display information. For example, the collection unit collects customer traffic flow data and product display status using in-store cameras and sensors. The collection unit can also collect data such as which aisles customers pass through and which products they are interested in. For example, the collection unit can record customer traffic flow using a ceiling camera and detect product display status using an infrared sensor. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze customer behavior patterns using AI. The AI ​​can analyze customer traffic flow data and product display information using machine learning algorithms and data mining techniques. For example, the analysis unit analyzes customer movement paths and dwell times to identify customer behavior patterns. The proposal unit proposes efficient and attractive displays and layouts based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes placing popular products in aisles frequently visited by customers. The proposal unit can also propose innovative product layouts. For example, the suggestion unit may suggest widening aisles to allow customers to find products more easily. This allows the store design suggestion system according to the embodiment to improve customer experience and sales efficiency. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may make suggestions using an AI model that receives the analysis results obtained by the analysis unit as input and outputs efficient and attractive displays and placements.

[0030] The collection unit can collect customer movement data and product placement status using in-store cameras or sensors. The collection unit, for example, collects customer movement data using in-store cameras. For example, the collection unit records customer movement routes using a ceiling camera. The collection unit can also collect product placement status using a sensor. For example, the collection unit detects product placement locations using an infrared sensor. The collection unit can also collect data such as which aisles customers pass through and which products they show interest in. For example, the collection unit records the length of time customers stay and collects data on products they show interest in. This makes it possible to collect detailed customer movement data and product placement status. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired by a camera or sensor into a generation AI and have the generation AI analyze the movement data and product placement status.

[0031] The analysis unit can use AI to analyze customer behavior patterns based on the collected data. The analysis unit, for example, analyzes customer behavior patterns using AI. For example, the analysis unit can analyze customer movement data using a machine learning algorithm to identify behavior patterns. The analysis unit can also analyze customer behavior patterns using data mining technology. For example, the analysis unit can analyze customer movement paths and stay times to identify behavior patterns. The analysis unit can also analyze customer behavior patterns in real time using AI. For example, the analysis unit can instantly analyze customer behavior patterns based on data collected in real time. This allows for detailed analysis of customer behavior patterns. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the behavior patterns.

[0032] The suggestion unit can make a suggestion to place popular products in aisles frequently visited by customers based on the analysis results. The suggestion unit, for example, makes a suggestion to place popular products in aisles frequently visited by customers based on the analysis results. For example, the suggestion unit identifies placement locations for popular products based on customer movement data. The suggestion unit can also make a suggestion to optimize the placement of popular products based on customer behavior patterns. For example, the suggestion unit makes a suggestion to improve sales efficiency by placing popular products in aisles frequently visited by customers. The suggestion unit can also make a suggestion to improve product placement to increase customer purchasing motivation. For example, the suggestion unit makes a suggestion to widen aisles so that customers can easily find products. This allows popular products to be placed in aisles frequently visited by customers, thereby improving sales efficiency. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the analysis results to a generation AI and cause the generation AI to execute a placement suggestion for popular products.

[0033] The suggestion unit can make a proposal to optimize product placement. The suggestion unit, for example, makes a proposal to optimize product placement. For example, the suggestion unit makes a proposal to optimize product placement based on customer movement line data. The suggestion unit can also make a proposal to improve product placement based on customer behavior patterns. For example, the suggestion unit makes a proposal to improve product placement so that customers can find products smoothly. The suggestion unit can also make a proposal to optimize product placement to increase customer purchasing motivation. For example, the suggestion unit makes a proposal to optimize product placement by taking into account customer line of sight guidance. This can increase customer purchasing motivation by improving product placement. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input movement line data and behavior patterns into a generation AI and cause the generation AI to execute a product placement proposal.

[0034] The suggestion unit can make suggestions to expand aisles so that customers can find products more easily. The suggestion unit, for example, makes suggestions to expand aisles so that customers can find products more easily. For example, the suggestion unit can suggest expanding aisles based on customer movement line data. The suggestion unit can also suggest expanding aisles based on customer behavior patterns. For example, the suggestion unit can suggest widening aisles so that customers can find products more easily. The suggestion unit can also suggest expanding aisles to increase customer satisfaction. For example, the suggestion unit can suggest widening aisles so that customers can shop more comfortably. This can increase customer satisfaction by allowing customers to find products more easily. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input movement line data and behavior patterns into a generation AI and cause the generation AI to execute a suggestion to expand aisles.

[0035] The collection unit can monitor the congestion status in the store in real time and adjust the frequency of data collection according to the level of congestion. For example, the collection unit can monitor the congestion status in the store in real time and adjust the frequency of data collection according to the level of congestion. For example, when the store is crowded, the collection unit can reduce the frequency of data collection to allow customers to move more naturally. The collection unit can also increase the frequency of data collection to obtain detailed movement line data when the store is empty. For example, when the congestion level is moderate, the collection unit can moderately adjust the frequency of data collection to collect balanced data. In this way, by adjusting the frequency of data collection according to the level of congestion, it is possible to allow customers to move more naturally. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input congestion status data to a generation AI and cause the generation AI to adjust the frequency of data collection.

[0036] The collection unit can link a customer's purchase history with the movement data to collect data for increasing interest in a specific product. For example, the collection unit links a customer's purchase history with the movement data to collect data for increasing interest in a specific product. For example, the collection unit prioritizes collecting movement data related to products the customer has previously purchased. The collection unit can also estimate the customer's interest in a specific product from the customer's purchase history and collect movement data around the product. For example, the collection unit collects detailed movement data for products the customer frequently purchases and acquires data for increasing interest. In this way, by linking the customer's purchase history with the movement data, data for increasing interest in a specific product can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the purchase history data and the movement data into a generation AI and cause the generation AI to collect data for increasing interest.

[0037] The collection unit can collect customer movement data in response to changes in temperature and lighting in the store. The collection unit collects customer movement data in response to changes in temperature and lighting in the store, for example. For example, when the temperature in the store rises, the collection unit collects customer movement data and analyzes the impact of the temperature change on behavior. The collection unit can also collect customer movement data when the lighting becomes brighter and analyze the impact of the lighting change on behavior. For example, when changes in temperature and lighting occur simultaneously, the collection unit collects customer movement data and analyzes the combined impact. In this way, by collecting customer movement data in response to changes in temperature and lighting in the store, it is possible to analyze the impact of environmental changes on behavior. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired by a temperature sensor or illuminance sensor into a generation AI and cause the generation AI to collect movement data.

[0038] The collection unit can collect customer movement data taking into account the acoustic environment in the store. The collection unit collects customer movement data taking into account, for example, the acoustic environment in the store. For example, when the music in the store changes, the collection unit collects customer movement data and analyzes the impact of the music. The collection unit can also collect customer movement data and analyze the impact of the noise when the noise level in the store increases. For example, when the acoustic environment in the store becomes quieter, the collection unit collects customer movement data and analyzes the impact of the silence. In this way, by collecting customer movement data taking into account the acoustic environment in the store, it is possible to analyze the impact of the acoustic environment on behavior. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input acoustic environment data to a generation AI and cause the generation AI to collect movement data.

[0039] The collection unit can collect movement line data based on attribute information such as customer age and gender. The collection unit collects movement line data based on attribute information such as customer age and gender. For example, the collection unit may preferentially collect movement line data of younger customers and analyze behavior patterns for each age group. The collection unit may also preferentially collect movement line data of female customers and analyze behavior patterns for each gender. For example, the collection unit may preferentially collect movement line data of elderly customers and analyze behavior patterns for each age group. In this way, by collecting movement line data based on customer attribute information, behavior patterns for each attribute can be analyzed. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input attribute information data to a generation AI and cause the generation AI to collect movement line data.

[0040] The collection unit can collect customer movement data taking into account the impact of a promotional event in a store. The collection unit collects customer movement data taking into account the impact of a promotional event in a store, for example. For example, the collection unit collects customer movement data when a promotional event is held and analyzes the impact of the event. The collection unit can also collect customer movement data after the promotional event has ended and analyze the impact of the event. For example, the collection unit compares customer movement data before and after the promotional event and analyzes the impact of the event. In this way, the impact of the event can be analyzed by collecting customer movement data taking into account the impact of the promotional event. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input promotional event data into a generation AI and cause the generation AI to collect movement data.

[0041] The analysis unit can combine a customer's purchase history and movement data to analyze the level of interest in a specific product. For example, the analysis unit combines a customer's purchase history and movement data to analyze the level of interest in a specific product. For example, the analysis unit estimates the level of interest in a specific product from the customer's purchase history and analyzes the movement data around the product. The analysis unit can also perform a detailed analysis of the movement data for products that the customer frequently purchases to obtain data to increase the level of interest. For example, the analysis unit combines a customer's purchase history and movement data to analyze the level of interest in a specific product and develop a sales strategy. In this way, the combination of a customer's purchase history and movement data can analyze the level of interest in a specific product. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the purchase history data and movement data into a generation AI and have the generation AI perform an interest level analysis.

[0042] The analysis unit can analyze the congestion status of the store and identify customer behavior patterns according to the level of congestion. The analysis unit, for example, analyzes the congestion status of the store and identifies customer behavior patterns according to the level of congestion. For example, when the store is crowded, the analysis unit analyzes customer behavior patterns and identifies behavioral characteristics when the store is crowded. The analysis unit can also analyze customer behavior patterns when the store is empty and identify behavioral characteristics when the store is empty. For example, when the congestion level is moderate, the analysis unit analyzes customer behavior patterns and identifies balanced behavioral characteristics. This makes it possible to analyze behavioral characteristics when the store is crowded and when the store is empty by identifying customer behavior patterns according to the level of congestion. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input congestion status data to a generation AI and have the generation AI identify behavioral patterns.

[0043] The analysis unit can analyze the impact of changes in temperature and lighting in the store on customer behavior. The analysis unit, for example, analyzes the impact of changes in temperature and lighting in the store on customer behavior. For example, when the temperature in the store rises, the analysis unit analyzes customer behavior patterns and identifies the impact of temperature changes on behavior. The analysis unit can also analyze customer behavior patterns when the lighting is brightened and identify the impact of lighting changes on behavior. For example, when changes in temperature and lighting occur simultaneously, the analysis unit analyzes customer behavior patterns and identifies the combined impact. In this way, by analyzing the impact of changes in temperature and lighting in the store on customer behavior, it is possible to identify the impact of environmental changes on behavior. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data acquired by a temperature sensor or illuminance sensor into a generation AI and have the generation AI analyze behavior patterns.

[0044] The analysis unit can analyze the impact of the in-store acoustic environment on customer behavior. The analysis unit, for example, analyzes the impact of the in-store acoustic environment on customer behavior. For example, when the music in the store changes, the analysis unit analyzes customer behavior patterns to identify the impact of the music. The analysis unit can also analyze customer behavior patterns to identify the impact of the noise when the noise level in the store increases. For example, when the in-store acoustic environment becomes quieter, the analysis unit analyzes customer behavior patterns to identify the impact of the silence. In this way, by analyzing the impact of the in-store acoustic environment on customer behavior, it is possible to identify the impact of the acoustic environment on behavior. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input acoustic environment data to a generation AI and cause the generation AI to analyze behavior patterns.

[0045] The analysis unit can analyze behavioral patterns based on attribute information such as the customer's age and gender. The analysis unit analyzes behavioral patterns based on attribute information such as the customer's age and gender. For example, the analysis unit may prioritize analyzing the behavioral patterns of younger customers to identify behavioral characteristics for each age group. The analysis unit may also prioritize analyzing the behavioral patterns of female customers to identify behavioral characteristics for each gender. For example, the analysis unit may prioritize analyzing the behavioral patterns of elderly customers to identify behavioral characteristics for each age group. In this way, by analyzing behavioral patterns based on customer attribute information, behavioral characteristics for each attribute can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input attribute information data to a generation AI and cause the generation AI to analyze the behavioral patterns.

[0046] The analysis unit can analyze the impact of an in-store promotional event on customer behavior. The analysis unit, for example, analyzes the impact of an in-store promotional event on customer behavior. For example, the analysis unit analyzes customer behavior patterns when a promotional event is held to identify the impact of the event. The analysis unit can also analyze customer behavior patterns after the promotional event has ended to identify the impact of the event. For example, the analysis unit compares customer behavior patterns before and after the promotional event to identify the impact of the event. In this way, the impact of the event can be identified by analyzing the impact of the promotional event on customer behavior. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input promotional event data to a generation AI and have the generation AI analyze the behavioral patterns.

[0047] The suggestion unit can suggest where to place a specific product based on the customer's purchase history and movement line data. The suggestion unit can suggest where to place a specific product based on, for example, the customer's purchase history and movement line data. For example, the suggestion unit can estimate the customer's interest in a specific product based on the customer's purchase history and suggest placing the product in the optimal location. The suggestion unit can also suggest placing a product that the customer frequently purchases in the most accessible location based on the movement line data. For example, the suggestion unit can combine the customer's purchase history and movement line data to suggest placing a specific product in a prominent location. This can improve sales efficiency by suggesting where to place a specific product in the optimal location based on the customer's purchase history and movement line data. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the purchase history data and movement line data into a generation AI and cause the generation AI to execute a product placement proposal.

[0048] The suggestion unit can propose a display or arrangement to avoid congestion, taking into account the congestion level in the store. The suggestion unit, for example, proposes a display or arrangement to avoid congestion, taking into account the congestion level in the store. For example, when the store is crowded, the suggestion unit proposes dispersing product placement to avoid congestion. Furthermore, when the store is empty, the suggestion unit can also propose an arrangement that allows customers to easily find products. For example, when the store is moderately crowded, the suggestion unit proposes an arrangement to smooth customer flow. In this way, by proposing a display or arrangement taking into account the congestion level, it is possible to smooth customer flow. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input congestion level data into a generation AI and cause the generation AI to execute display or arrangement suggestions.

[0049] The suggestion unit can propose optimal display and arrangement in response to changes in temperature and lighting in the store. The suggestion unit proposes optimal display and arrangement in response to changes in temperature and lighting in the store, for example. For example, if the temperature in the store rises, the suggestion unit proposes a product arrangement that will make customers feel comfortable. The suggestion unit can also propose an arrangement that will improve the appearance of products in response to changes in lighting. For example, if changes in temperature and lighting occur simultaneously, the suggestion unit proposes an arrangement that will maintain customer comfort. In this way, customer comfort can be maintained by proposing display and arrangement in response to changes in temperature and lighting in the store. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data acquired by a temperature sensor or an illuminance sensor into the generation AI and cause the generation AI to execute display and arrangement suggestions.

[0050] The suggestion unit can propose a display and arrangement that customers find comfortable, taking into account the acoustic environment in the store. The suggestion unit proposes a display and arrangement that customers find comfortable, taking into account, for example, the acoustic environment in the store. For example, when the music in the store changes, the suggestion unit proposes a product display and arrangement that customers find comfortable. The suggestion unit can also propose a product display and arrangement that customers find comfortable, if the noise level in the store increases. For example, when the acoustic environment in the store becomes quieter, the suggestion unit proposes a product display and arrangement that customers find comfortable. In this way, customer comfort can be maintained by proposing a display and arrangement that takes into account the acoustic environment in the store. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit inputs acoustic environment data into a generation AI and causes the generation AI to execute a display and arrangement proposal.

[0051] The suggestion unit can propose optimal display and placement based on attribute information such as the customer's age and gender. The suggestion unit proposes optimal display and placement based on attribute information such as the customer's age and gender. For example, the suggestion unit proposes display and placement of products that will attract the interest of younger customers. The suggestion unit can also propose display and placement of products that will increase purchasing motivation for female customers. For example, the suggestion unit proposes display and placement of products that are easy to see and access for elderly customers. In this way, by proposing display and placement based on customer attribute information, purchasing motivation for each attribute can be increased. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input attribute information data into a generation AI and cause the generation AI to execute display and placement suggestions.

[0052] The suggestion unit can propose a display and arrangement to highlight a specific product in accordance with a promotional event held in the store. The suggestion unit, for example, proposes a display and arrangement to highlight a specific product in accordance with a promotional event held in the store. For example, the suggestion unit proposes a display and arrangement to highlight a specific product when a promotional event is held. The suggestion unit can also propose a display and arrangement to highlight a specific product after the promotional event has ended. For example, the suggestion unit proposes a display and arrangement to highlight a specific product before and after the promotional event. This makes it possible to maximize the effectiveness of the event by proposing a display and arrangement to highlight a specific product in accordance with the promotional event. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input promotional event data into a generation AI and cause the generation AI to execute display and arrangement suggestions.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The suggestion unit can combine the customer's purchase history and movement line data to suggest where to place a specific product. For example, the suggestion unit can estimate the customer's interest in a specific product from their purchase history and suggest placing that product in the optimal location. The suggestion unit can also suggest placing a product that the customer frequently purchases in the most accessible location based on the movement line data. Furthermore, the suggestion unit combines the customer's purchase history and movement line data to suggest placing a specific product in a prominent location. In this way, sales efficiency can be improved by suggesting the optimal location of a specific product based on the customer's purchase history and movement line data.

[0055] The collection unit can monitor the congestion status of the store in real time and adjust the frequency of data collection according to the level of congestion. For example, when the store is crowded, the collection unit reduces the frequency of data collection to allow customers to move around more naturally. The collection unit can also increase the frequency of data collection to obtain detailed movement data when the store is empty. Furthermore, when the level of congestion is moderate, the collection unit adjusts the frequency of data collection appropriately to collect balanced data. In this way, by adjusting the frequency of data collection according to the level of congestion, it is possible to allow customers to move around more naturally.

[0056] The suggestion unit can suggest optimal display and arrangement in response to changes in the temperature and lighting in the store. For example, if the temperature in the store rises, the suggestion unit suggests devising a product arrangement that will make customers feel comfortable. Also, if the lighting becomes brighter, the suggestion unit can suggest an arrangement that will make the products look better. Furthermore, if changes in temperature and lighting occur simultaneously, the suggestion unit suggests an arrangement that will maintain customer comfort. In this way, customer comfort can be maintained by suggesting display and arrangement in response to changes in the temperature and lighting in the store.

[0057] The analysis unit can analyze the congestion status of the store and identify customer behavior patterns according to the level of congestion. For example, when the store is crowded, the analysis unit analyzes customer behavior patterns and identifies behavioral characteristics when the store is crowded. The analysis unit can also analyze customer behavior patterns when the store is empty and identify behavioral characteristics when the store is empty. Furthermore, when the level of congestion is medium, the analysis unit analyzes customer behavior patterns and identifies balanced behavioral characteristics. In this way, by identifying customer behavior patterns according to the level of congestion, it is possible to analyze behavioral characteristics when the store is crowded and when it is empty.

[0058] The suggestion unit can suggest a display or arrangement to highlight a specific product in accordance with a promotional event held in the store. For example, the suggestion unit suggests a display or arrangement to highlight a specific product when a promotional event is held. The suggestion unit can also suggest a display or arrangement to highlight a specific product after the promotional event has ended. Furthermore, the suggestion unit suggests a display or arrangement to highlight a specific product before and after the promotional event. This makes it possible to maximize the effectiveness of the event by suggesting a display or arrangement to highlight a specific product in accordance with the promotional event.

[0059] The suggestion unit can propose optimal display and placement based on attribute information such as customer age and gender. For example, the suggestion unit can propose product display and placement that will attract the interest of younger customers. The suggestion unit can also propose product display and placement that will increase purchasing motivation for female customers. Furthermore, the suggestion unit can propose product display and placement that is easy to see and access for elderly customers. In this way, by proposing display and placement based on customer attribute information, it is possible to increase purchasing motivation for each attribute.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects in-store traffic flow data and product display information. For example, in-store cameras and sensors are used to collect customer traffic flow data and product placement status. Data such as which aisles customers passed through and which products they showed interest in is also collected. Specifically, ceiling cameras are used to record customer traffic flow, and infrared sensors are used to detect product placement status. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses AI to analyze customer behavior patterns. It uses machine learning algorithms and data mining technology to analyze customer movement data and product display information to identify customer movement routes and length of stay. Step 3: The proposal unit proposes efficient and attractive displays and layouts based on the analysis results obtained by the analysis unit. For example, it makes proposals to place popular products in aisles that customers frequently pass through, or to improve product layout. Specifically, it makes proposals to widen aisles so that customers can easily find products. The proposal unit's processing may be performed using AI, and proposals can be made using an AI model that takes the analysis results as input and outputs efficient and attractive displays and layouts.

[0062] (Example 2) A store design proposal system according to an embodiment of the present invention proposes an efficient and attractive store design based on in-store traffic flow data and product display information. The store design proposal system collects in-store traffic flow data and product display information, analyzes them using AI, and proposes efficient and attractive displays and layouts. For example, the store design proposal system collects customer traffic flow data and product display information using in-store cameras and sensors. Next, the store design proposal system analyzes the collected data using AI to understand customer behavior patterns. Furthermore, based on the analysis results, the store design proposal system proposes placing popular products in aisles frequently visited by customers. This allows the store design proposal system to improve customer experience and sales efficiency. This allows the store design proposal system to realize an optimal store design backed by data and strengthen competitiveness. For example, by proposing efficient displays and layouts based on customer traffic flow data, customer experience and sales efficiency can be improved. Furthermore, by carefully arranging products, customer purchasing motivation can be increased. This allows the store design proposal system to strengthen competitiveness and increase sales.

[0063] A store design proposal system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects in-store traffic flow data and product display information. For example, the collection unit collects customer traffic flow data and product display status using in-store cameras and sensors. The collection unit can also collect data such as which aisles customers pass through and which products they are interested in. For example, the collection unit can record customer traffic flow using a ceiling camera and detect product display status using an infrared sensor. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze customer behavior patterns using AI. The AI ​​can analyze customer traffic flow data and product display information using machine learning algorithms and data mining techniques. For example, the analysis unit analyzes customer movement paths and dwell times to identify customer behavior patterns. The proposal unit proposes efficient and attractive displays and layouts based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes placing popular products in aisles frequently visited by customers. The proposal unit can also propose innovative product layouts. For example, the suggestion unit may suggest widening aisles to allow customers to find products more easily. This allows the store design suggestion system according to the embodiment to improve customer experience and sales efficiency. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may make suggestions using an AI model that receives the analysis results obtained by the analysis unit as input and outputs efficient and attractive displays and placements.

[0064] The collection unit can collect customer movement data and product placement status using in-store cameras or sensors. The collection unit, for example, collects customer movement data using in-store cameras. For example, the collection unit records customer movement routes using a ceiling camera. The collection unit can also collect product placement status using a sensor. For example, the collection unit detects product placement locations using an infrared sensor. The collection unit can also collect data such as which aisles customers pass through and which products they show interest in. For example, the collection unit records the length of time customers stay and collects data on products they show interest in. This makes it possible to collect detailed customer movement data and product placement status. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired by a camera or sensor into a generation AI and have the generation AI analyze the movement data and product placement status.

[0065] The analysis unit can use AI to analyze customer behavior patterns based on the collected data. The analysis unit, for example, analyzes customer behavior patterns using AI. For example, the analysis unit can analyze customer movement data using a machine learning algorithm to identify behavior patterns. The analysis unit can also analyze customer behavior patterns using data mining technology. For example, the analysis unit can analyze customer movement paths and stay times to identify behavior patterns. The analysis unit can also analyze customer behavior patterns in real time using AI. For example, the analysis unit can instantly analyze customer behavior patterns based on data collected in real time. This allows for detailed analysis of customer behavior patterns. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the behavior patterns.

[0066] The suggestion unit can make a suggestion to place popular products in aisles frequently visited by customers based on the analysis results. The suggestion unit, for example, makes a suggestion to place popular products in aisles frequently visited by customers based on the analysis results. For example, the suggestion unit identifies placement locations for popular products based on customer movement data. The suggestion unit can also make a suggestion to optimize the placement of popular products based on customer behavior patterns. For example, the suggestion unit makes a suggestion to improve sales efficiency by placing popular products in aisles frequently visited by customers. The suggestion unit can also make a suggestion to improve product placement to increase customer purchasing motivation. For example, the suggestion unit makes a suggestion to widen aisles so that customers can easily find products. This allows popular products to be placed in aisles frequently visited by customers, thereby improving sales efficiency. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the analysis results to a generation AI and cause the generation AI to execute a placement suggestion for popular products.

[0067] The suggestion unit can make a proposal to optimize product placement. The suggestion unit, for example, makes a proposal to optimize product placement. For example, the suggestion unit makes a proposal to optimize product placement based on customer movement line data. The suggestion unit can also make a proposal to improve product placement based on customer behavior patterns. For example, the suggestion unit makes a proposal to improve product placement so that customers can find products smoothly. The suggestion unit can also make a proposal to optimize product placement to increase customer purchasing motivation. For example, the suggestion unit makes a proposal to optimize product placement by taking into account customer line of sight guidance. This can increase customer purchasing motivation by improving product placement. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input movement line data and behavior patterns into a generation AI and cause the generation AI to execute a product placement proposal.

[0068] The suggestion unit can make suggestions to expand aisles so that customers can find products more easily. The suggestion unit, for example, makes suggestions to expand aisles so that customers can find products more easily. For example, the suggestion unit can suggest expanding aisles based on customer movement line data. The suggestion unit can also suggest expanding aisles based on customer behavior patterns. For example, the suggestion unit can suggest widening aisles so that customers can find products more easily. The suggestion unit can also suggest expanding aisles to increase customer satisfaction. For example, the suggestion unit can suggest widening aisles so that customers can shop more comfortably. This can increase customer satisfaction by allowing customers to find products more easily. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input movement line data and behavior patterns into a generation AI and cause the generation AI to execute a suggestion to expand aisles.

[0069] The collection unit can estimate a customer's emotions and adjust the timing of collecting movement line data based on the emotion data. For example, the collection unit estimates a customer's emotions and adjusts the timing of collecting movement line data based on the emotion data. For example, if a customer is feeling stressed, the collection unit reduces the frequency of collecting movement line data and observes the customer's behavior naturally. Furthermore, if a customer is relaxed, the collection unit can increase the frequency of collecting movement line data to obtain detailed behavioral patterns. For example, if a customer is excited, the collection unit moves the timing of collecting movement line data closer to real time to capture instantaneous behavioral changes. This allows for more accurate data collection by adjusting the timing of collecting movement line data according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input customer emotion data into the generation AI and have the generation AI adjust the timing of collecting movement line data.

[0070] The collection unit can monitor the congestion status in the store in real time and adjust the frequency of data collection according to the level of congestion. For example, the collection unit can monitor the congestion status in the store in real time and adjust the frequency of data collection according to the level of congestion. For example, when the store is crowded, the collection unit can reduce the frequency of data collection to allow customers to move more naturally. The collection unit can also increase the frequency of data collection to obtain detailed movement line data when the store is empty. For example, when the congestion level is moderate, the collection unit can moderately adjust the frequency of data collection to collect balanced data. In this way, by adjusting the frequency of data collection according to the level of congestion, it is possible to allow customers to move more naturally. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input congestion status data to a generation AI and cause the generation AI to adjust the frequency of data collection.

[0071] The collection unit can link a customer's purchase history with the movement data to collect data for increasing interest in a specific product. For example, the collection unit links a customer's purchase history with the movement data to collect data for increasing interest in a specific product. For example, the collection unit prioritizes collecting movement data related to products the customer has previously purchased. The collection unit can also estimate the customer's interest in a specific product from the customer's purchase history and collect movement data around the product. For example, the collection unit collects detailed movement data for products the customer frequently purchases and acquires data for increasing interest. In this way, by linking the customer's purchase history with the movement data, data for increasing interest in a specific product can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the purchase history data and the movement data into a generation AI and cause the generation AI to collect data for increasing interest.

[0072] The collection unit can collect customer movement data in response to changes in temperature and lighting in the store. The collection unit collects customer movement data in response to changes in temperature and lighting in the store, for example. For example, when the temperature in the store rises, the collection unit collects customer movement data and analyzes the impact of the temperature change on behavior. The collection unit can also collect customer movement data when the lighting becomes brighter and analyze the impact of the lighting change on behavior. For example, when changes in temperature and lighting occur simultaneously, the collection unit collects customer movement data and analyzes the combined impact. In this way, by collecting customer movement data in response to changes in temperature and lighting in the store, it is possible to analyze the impact of environmental changes on behavior. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired by a temperature sensor or illuminance sensor into a generation AI and cause the generation AI to collect movement data.

[0073] The collection unit can estimate a customer's emotions and prioritize data to be collected based on the estimated customer emotions. The collection unit, for example, estimates a customer's emotions and prioritizes data to be collected based on the estimated customer emotions. For example, if a customer is feeling stressed, the collection unit can prioritize collecting data related to stress reduction. Furthermore, if a customer is relaxed, the collection unit can prioritize collecting data for maintaining a relaxed state. For example, if a customer is excited, the collection unit can prioritize collecting data for analyzing the excited state. Thus, by prioritizing data to be collected based on the customer's emotions, more important data can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI. For example, the collection unit can input customer emotion data into the generation AI and have the generation AI prioritize the data.

[0074] The collection unit can collect customer movement data taking into account the acoustic environment in the store. The collection unit collects customer movement data taking into account, for example, the acoustic environment in the store. For example, when the music in the store changes, the collection unit collects customer movement data and analyzes the impact of the music. The collection unit can also collect customer movement data and analyze the impact of the noise when the noise level in the store increases. For example, when the acoustic environment in the store becomes quieter, the collection unit collects customer movement data and analyzes the impact of the silence. In this way, by collecting customer movement data taking into account the acoustic environment in the store, it is possible to analyze the impact of the acoustic environment on behavior. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input acoustic environment data to a generation AI and cause the generation AI to collect movement data.

[0075] The collection unit can collect movement line data based on attribute information such as customer age and gender. The collection unit collects movement line data based on attribute information such as customer age and gender. For example, the collection unit may preferentially collect movement line data of younger customers and analyze behavior patterns for each age group. The collection unit may also preferentially collect movement line data of female customers and analyze behavior patterns for each gender. For example, the collection unit may preferentially collect movement line data of elderly customers and analyze behavior patterns for each age group. In this way, by collecting movement line data based on customer attribute information, behavior patterns for each attribute can be analyzed. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input attribute information data to a generation AI and cause the generation AI to collect movement line data.

[0076] The collection unit can collect customer movement data taking into account the impact of a promotional event in a store. The collection unit collects customer movement data taking into account the impact of a promotional event in a store, for example. For example, the collection unit collects customer movement data when a promotional event is held and analyzes the impact of the event. The collection unit can also collect customer movement data after the promotional event has ended and analyze the impact of the event. For example, the collection unit compares customer movement data before and after the promotional event and analyzes the impact of the event. In this way, the impact of the event can be analyzed by collecting customer movement data taking into account the impact of the promotional event. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input promotional event data into a generation AI and cause the generation AI to collect movement data.

[0077] The analysis unit can estimate the customer's emotions and adjust the analysis method of the behavioral patterns based on the estimated customer emotions. The analysis unit, for example, estimates the customer's emotions and adjusts the analysis method of the behavioral patterns based on the estimated customer emotions. For example, if the customer is feeling stressed, the analysis unit prioritizes analyzing behavioral patterns related to stress reduction. Furthermore, if the customer is relaxed, the analysis unit can prioritize analyzing behavioral patterns for maintaining a relaxed state. For example, if the customer is excited, the analysis unit prioritizes analyzing behavioral patterns for analyzing an excited state. This allows for adjusting the analysis method of the behavioral patterns according to the customer's emotions to obtain more accurate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input customer emotion data into the generation AI and cause the generation AI to adjust the analysis method of the behavioral patterns.

[0078] The analysis unit can combine a customer's purchase history and movement data to analyze the level of interest in a specific product. For example, the analysis unit combines a customer's purchase history and movement data to analyze the level of interest in a specific product. For example, the analysis unit estimates the level of interest in a specific product from the customer's purchase history and analyzes the movement data around the product. The analysis unit can also perform a detailed analysis of the movement data for products that the customer frequently purchases to obtain data to increase the level of interest. For example, the analysis unit combines a customer's purchase history and movement data to analyze the level of interest in a specific product and develop a sales strategy. In this way, the combination of a customer's purchase history and movement data can analyze the level of interest in a specific product. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the purchase history data and movement data into a generation AI and have the generation AI perform an interest level analysis.

[0079] The analysis unit can analyze the congestion status of the store and identify customer behavior patterns according to the level of congestion. The analysis unit, for example, analyzes the congestion status of the store and identifies customer behavior patterns according to the level of congestion. For example, when the store is crowded, the analysis unit analyzes customer behavior patterns and identifies behavioral characteristics when the store is crowded. The analysis unit can also analyze customer behavior patterns when the store is empty and identify behavioral characteristics when the store is empty. For example, when the congestion level is moderate, the analysis unit analyzes customer behavior patterns and identifies balanced behavioral characteristics. This makes it possible to analyze behavioral characteristics when the store is crowded and when the store is empty by identifying customer behavior patterns according to the level of congestion. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input congestion status data to a generation AI and have the generation AI identify behavioral patterns.

[0080] The analysis unit can analyze the impact of changes in temperature and lighting in the store on customer behavior. The analysis unit, for example, analyzes the impact of changes in temperature and lighting in the store on customer behavior. For example, when the temperature in the store rises, the analysis unit analyzes customer behavior patterns and identifies the impact of temperature changes on behavior. The analysis unit can also analyze customer behavior patterns when the lighting is brightened and identify the impact of lighting changes on behavior. For example, when changes in temperature and lighting occur simultaneously, the analysis unit analyzes customer behavior patterns and identifies the combined impact. In this way, by analyzing the impact of changes in temperature and lighting in the store on customer behavior, it is possible to identify the impact of environmental changes on behavior. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data acquired by a temperature sensor or illuminance sensor into a generation AI and have the generation AI analyze behavior patterns.

[0081] The analysis unit can estimate the customer's emotions and adjust the display method of the analysis results based on the estimated customer emotions. For example, the analysis unit can estimate the customer's emotions and adjust the display method of the analysis results based on the estimated customer emotions. For example, if the customer is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the customer is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the customer is excited, the analysis unit can provide a visually stimulating display method. By adjusting the display method of the analysis results according to the customer's emotions, a more visible display method can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input customer emotion data into the generation AI and have the generation AI adjust the display method.

[0082] The analysis unit can analyze the impact of the in-store acoustic environment on customer behavior. The analysis unit, for example, analyzes the impact of the in-store acoustic environment on customer behavior. For example, when the music in the store changes, the analysis unit analyzes customer behavior patterns to identify the impact of the music. The analysis unit can also analyze customer behavior patterns to identify the impact of the noise when the noise level in the store increases. For example, when the in-store acoustic environment becomes quieter, the analysis unit analyzes customer behavior patterns to identify the impact of the silence. In this way, by analyzing the impact of the in-store acoustic environment on customer behavior, it is possible to identify the impact of the acoustic environment on behavior. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input acoustic environment data to a generation AI and cause the generation AI to analyze behavior patterns.

[0083] The analysis unit can analyze behavioral patterns based on attribute information such as the customer's age and gender. The analysis unit analyzes behavioral patterns based on attribute information such as the customer's age and gender. For example, the analysis unit may prioritize analyzing the behavioral patterns of younger customers to identify behavioral characteristics for each age group. The analysis unit may also prioritize analyzing the behavioral patterns of female customers to identify behavioral characteristics for each gender. For example, the analysis unit may prioritize analyzing the behavioral patterns of elderly customers to identify behavioral characteristics for each age group. In this way, by analyzing behavioral patterns based on customer attribute information, behavioral characteristics for each attribute can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input attribute information data to a generation AI and cause the generation AI to analyze the behavioral patterns.

[0084] The analysis unit can analyze the impact of an in-store promotional event on customer behavior. The analysis unit, for example, analyzes the impact of an in-store promotional event on customer behavior. For example, the analysis unit analyzes customer behavior patterns when a promotional event is held to identify the impact of the event. The analysis unit can also analyze customer behavior patterns after the promotional event has ended to identify the impact of the event. For example, the analysis unit compares customer behavior patterns before and after the promotional event to identify the impact of the event. In this way, the impact of the event can be identified by analyzing the impact of the promotional event on customer behavior. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input promotional event data to a generation AI and have the generation AI analyze the behavioral patterns.

[0085] The suggestion unit can estimate a customer's emotions and adjust the display and layout suggestion method based on the estimated customer's emotions. The suggestion unit, for example, estimates a customer's emotions and adjusts the display and layout suggestion method based on the estimated customer's emotions. For example, if a customer is feeling stressed, the suggestion unit suggests a display and layout related to stress reduction. Furthermore, if a customer is relaxed, the suggestion unit can suggest a display and layout to maintain a relaxed state. For example, if a customer is excited, the suggestion unit suggests a display and layout that analyzes the excited state. This allows for more appropriate suggestions to be made by adjusting the display and layout suggestion method according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI. For example, the suggestion unit may input customer emotion data into the generation AI and cause the generation AI to adjust the suggestion method.

[0086] The suggestion unit can suggest where to place a specific product based on the customer's purchase history and movement line data. The suggestion unit can suggest where to place a specific product based on, for example, the customer's purchase history and movement line data. For example, the suggestion unit can estimate the customer's interest in a specific product based on the customer's purchase history and suggest placing the product in the optimal location. The suggestion unit can also suggest placing a product that the customer frequently purchases in the most accessible location based on the movement line data. For example, the suggestion unit can combine the customer's purchase history and movement line data to suggest placing a specific product in a prominent location. This can improve sales efficiency by suggesting where to place a specific product in the optimal location based on the customer's purchase history and movement line data. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the purchase history data and movement line data into a generation AI and cause the generation AI to execute a product placement proposal.

[0087] The suggestion unit can propose a display or arrangement to avoid congestion, taking into account the congestion level in the store. The suggestion unit, for example, proposes a display or arrangement to avoid congestion, taking into account the congestion level in the store. For example, when the store is crowded, the suggestion unit proposes dispersing product placement to avoid congestion. Furthermore, when the store is empty, the suggestion unit can also propose an arrangement that allows customers to easily find products. For example, when the store is moderately crowded, the suggestion unit proposes an arrangement to smooth customer flow. In this way, by proposing a display or arrangement taking into account the congestion level, it is possible to smooth customer flow. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input congestion level data into a generation AI and cause the generation AI to execute display or arrangement suggestions.

[0088] The suggestion unit can propose optimal display and arrangement in response to changes in temperature and lighting in the store. The suggestion unit proposes optimal display and arrangement in response to changes in temperature and lighting in the store, for example. For example, if the temperature in the store rises, the suggestion unit proposes a product arrangement that will make customers feel comfortable. The suggestion unit can also propose an arrangement that will improve the appearance of products in response to changes in lighting. For example, if changes in temperature and lighting occur simultaneously, the suggestion unit proposes an arrangement that will maintain customer comfort. In this way, customer comfort can be maintained by proposing display and arrangement in response to changes in temperature and lighting in the store. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data acquired by a temperature sensor or an illuminance sensor into the generation AI and cause the generation AI to execute display and arrangement suggestions.

[0089] The suggestion unit can estimate a customer's emotions and determine priorities for display and placement based on the estimated customer emotions. The suggestion unit, for example, estimates a customer's emotions and determines priorities for display and placement based on the estimated customer emotions. For example, if a customer is feeling stressed, the suggestion unit prioritizes the display and placement of products related to stress reduction. Furthermore, if a customer is relaxed, the suggestion unit can prioritize the display and placement of products to maintain a relaxed state. For example, if a customer is excited, the suggestion unit prioritizes the display and placement of products to analyze the excited state. In this way, by determining the priorities for display and placement based on the customer's emotions, it is possible to prioritize the display and placement of more important products. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input customer emotion data into the generation AI and have the generation AI determine the priorities.

[0090] The suggestion unit can propose a display and arrangement that customers find comfortable, taking into account the acoustic environment in the store. The suggestion unit proposes a display and arrangement that customers find comfortable, taking into account, for example, the acoustic environment in the store. For example, when the music in the store changes, the suggestion unit proposes a product display and arrangement that customers find comfortable. The suggestion unit can also propose a product display and arrangement that customers find comfortable, if the noise level in the store increases. For example, when the acoustic environment in the store becomes quieter, the suggestion unit proposes a product display and arrangement that customers find comfortable. In this way, customer comfort can be maintained by proposing a display and arrangement that takes into account the acoustic environment in the store. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit inputs acoustic environment data into a generation AI and causes the generation AI to execute a display and arrangement proposal.

[0091] The suggestion unit can propose optimal display and placement based on attribute information such as the customer's age and gender. The suggestion unit proposes optimal display and placement based on attribute information such as the customer's age and gender. For example, the suggestion unit proposes display and placement of products that will attract the interest of younger customers. The suggestion unit can also propose display and placement of products that will increase purchasing motivation for female customers. For example, the suggestion unit proposes display and placement of products that are easy to see and access for elderly customers. In this way, by proposing display and placement based on customer attribute information, purchasing motivation for each attribute can be increased. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input attribute information data into a generation AI and cause the generation AI to execute display and placement suggestions.

[0092] The suggestion unit can propose a display and arrangement to highlight a specific product in accordance with a promotional event held in the store. The suggestion unit, for example, proposes a display and arrangement to highlight a specific product in accordance with a promotional event held in the store. For example, the suggestion unit proposes a display and arrangement to highlight a specific product when a promotional event is held. The suggestion unit can also propose a display and arrangement to highlight a specific product after the promotional event has ended. For example, the suggestion unit proposes a display and arrangement to highlight a specific product before and after the promotional event. This makes it possible to maximize the effectiveness of the event by proposing a display and arrangement to highlight a specific product in accordance with the promotional event. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input promotional event data into a generation AI and cause the generation AI to execute display and arrangement suggestions. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect customer movement data and product placement status using the camera 42 or sensors of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests efficient and attractive displays and placements based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect customer movement data and product placement status using the camera 42 or sensors of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests efficient and attractive displays and placements based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and proposal unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect customer movement line data and product placement status using the camera 42 or sensors of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes efficient and attractive displays and placements based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect customer movement line data and product placement status using the camera 42 or sensors of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes efficient and attractive displays and placements based on the analysis results.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The suggestion unit can combine the customer's purchase history and movement line data to suggest where to place a specific product. For example, the suggestion unit can estimate the customer's interest in a specific product from their purchase history and suggest placing that product in the optimal location. The suggestion unit can also suggest placing a product that the customer frequently purchases in the most accessible location based on the movement line data. Furthermore, the suggestion unit combines the customer's purchase history and movement line data to suggest placing a specific product in a prominent location. In this way, sales efficiency can be improved by suggesting the optimal location of a specific product based on the customer's purchase history and movement line data.

[0095] The collection unit can monitor the congestion status of the store in real time and adjust the frequency of data collection according to the level of congestion. For example, when the store is crowded, the collection unit reduces the frequency of data collection to allow customers to move around more naturally. The collection unit can also increase the frequency of data collection to obtain detailed movement data when the store is empty. Furthermore, when the level of congestion is moderate, the collection unit adjusts the frequency of data collection appropriately to collect balanced data. In this way, by adjusting the frequency of data collection according to the level of congestion, it is possible to allow customers to move around more naturally.

[0096] The analysis unit can estimate the customer's emotions and adjust the behavioral pattern analysis method based on the estimated customer's emotions. For example, if the customer is feeling stressed, the analysis unit can prioritize analyzing behavioral patterns related to stress reduction. Also, if the customer is relaxed, the analysis unit can prioritize analyzing behavioral patterns for maintaining a relaxed state. Furthermore, if the customer is excited, the analysis unit prioritizes analyzing behavioral patterns for analyzing an excited state. In this way, by adjusting the behavioral pattern analysis method according to the customer's emotions, more accurate analysis results can be obtained.

[0097] The suggestion unit can suggest optimal display and arrangement in response to changes in the temperature and lighting in the store. For example, if the temperature in the store rises, the suggestion unit suggests devising a product arrangement that will make customers feel comfortable. Also, if the lighting becomes brighter, the suggestion unit can suggest an arrangement that will make the products look better. Furthermore, if changes in temperature and lighting occur simultaneously, the suggestion unit suggests an arrangement that will maintain customer comfort. In this way, customer comfort can be maintained by suggesting display and arrangement in response to changes in the temperature and lighting in the store.

[0098] The collection unit can estimate the customer's emotions and adjust the timing of collection of movement line data based on the emotion data. For example, if the customer is feeling stressed, the collection unit can reduce the frequency of collection of movement line data and observe the customer's behavior naturally. Also, if the customer is relaxed, the collection unit can increase the frequency of collection of movement line data and obtain detailed behavioral patterns. Furthermore, if the customer is excited, the collection unit can bring the timing of collection of movement line data closer to real time and capture instantaneous changes in behavior. This allows more accurate data to be collected by adjusting the timing of collection of movement line data according to the customer's emotions.

[0099] The suggestion unit can estimate the customer's emotions and adjust the display and layout suggestion method based on the estimated customer's emotions. For example, if the customer is feeling stressed, the suggestion unit can suggest a display and layout related to stress reduction. Also, if the customer is relaxed, the suggestion unit can suggest a display and layout for maintaining a relaxed state. Furthermore, if the customer is excited, the suggestion unit can suggest a display and layout for analyzing the excited state. In this way, by adjusting the display and layout suggestion method according to the customer's emotions, more appropriate suggestions can be made.

[0100] The analysis unit can analyze the congestion status of the store and identify customer behavior patterns according to the level of congestion. For example, when the store is crowded, the analysis unit analyzes customer behavior patterns and identifies behavioral characteristics when the store is crowded. The analysis unit can also analyze customer behavior patterns when the store is empty and identify behavioral characteristics when the store is empty. Furthermore, when the level of congestion is medium, the analysis unit analyzes customer behavior patterns and identifies balanced behavioral characteristics. In this way, by identifying customer behavior patterns according to the level of congestion, it is possible to analyze behavioral characteristics when the store is crowded and when it is empty.

[0101] The suggestion unit can suggest a display or arrangement to highlight a specific product in accordance with a promotional event held in the store. For example, the suggestion unit suggests a display or arrangement to highlight a specific product when a promotional event is held. The suggestion unit can also suggest a display or arrangement to highlight a specific product after the promotional event has ended. Furthermore, the suggestion unit suggests a display or arrangement to highlight a specific product before and after the promotional event. This makes it possible to maximize the effectiveness of the event by suggesting a display or arrangement to highlight a specific product in accordance with the promotional event.

[0102] The analysis unit can estimate the customer's emotions and adjust the display method of the analysis results based on the estimated customer emotions. For example, if the customer is feeling stressed, the analysis unit provides a simple, highly visible display method. If the customer is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, if the customer is excited, the analysis unit provides a visually stimulating display method. In this way, by adjusting the display method of the analysis results according to the customer's emotions, a more visible display method can be provided.

[0103] The suggestion unit can propose optimal display and placement based on attribute information such as customer age and gender. For example, the suggestion unit can propose product display and placement that will attract the interest of younger customers. The suggestion unit can also propose product display and placement that will increase purchasing motivation for female customers. Furthermore, the suggestion unit can propose product display and placement that is easy to see and access for elderly customers. In this way, by proposing display and placement based on customer attribute information, it is possible to increase purchasing motivation for each attribute.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit collects in-store traffic flow data and product display information. For example, in-store cameras and sensors are used to collect customer traffic flow data and product placement status. Data such as which aisles customers passed through and which products they showed interest in is also collected. Specifically, ceiling cameras are used to record customer traffic flow, and infrared sensors are used to detect product placement status. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses AI to analyze customer behavior patterns. It uses machine learning algorithms and data mining technology to analyze customer movement data and product display information to identify customer movement routes and length of stay. Step 3: The proposal unit proposes efficient and attractive displays and layouts based on the analysis results obtained by the analysis unit. For example, it makes proposals to place popular products in aisles that customers frequently pass through, or to improve product layout. Specifically, it makes proposals to widen aisles so that customers can easily find products. The proposal unit's processing may be performed using AI, and proposals can be made using an AI model that takes the analysis results as input and outputs efficient and attractive displays and layouts.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects in-store traffic flow data and product display information; an analysis unit that analyzes the data collected by the collection unit; a suggestion unit that suggests an efficient and attractive display or arrangement based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit In-store cameras or sensors are used to collect data on customer movement and product placement.

2. The system of claim 1.

3. The analysis unit AI analyzes customer behavior patterns based on collected data 2. The system of claim 1.

4. The proposal unit Based on the analysis results, it makes suggestions for placing popular products in aisles where customers frequently pass through.

2. The system of claim 1.

5. The proposal unit Make suggestions to optimize product placement 2. The system of claim 1.

6. The proposal unit Propose ways to widen aisles to help customers find products more easily 2. The system of claim 1.

7. The collecting unit Estimate customer sentiment and adjust the timing of collection of movement data based on the estimated sentiment 2. The system of claim 1.

8. The collecting unit Monitor in-store congestion in real time and adjust data collection frequency depending on the level of congestion.

2. The system of claim 1.

Images